1. How a Semantic Layer Makes
Data Mesh Work at Scale
Dataversity
Elif Tutuk, Global Head of Product at AtScale
February 7th, 2023

2. 2
The data mesh is an approach to
building a decentralized analytics
architecture where business
domains are responsible for their
data – giving ownership to the group
that's closest to and best
understands the data.
Successful data mesh requires:
- Flexibility and agility
- Governance and single version
of truth
- Abstract technical complexity
Data Mesh

3. Data Types
Operational
Data
running the business
Analytical
Data
optimizing the
business
Captures current
state of applications
Transactional
Optimized for
application logic
Optimized for analytics
Multi dimensional analysis,
breakdown, KPIs, ML
training
Historical
ETL

4. The data journey…
Operational
Data
Analytical
Data
ETL
Data Warehouse
Data Warehouse
Accessed
via SQL
and APIs
Data Lake
ELT
Analytical data
Accessed via
SQL and APIs
Operational data
Data Lake

5. Low analytics adoption and fractionated data driven decisions
1
Centralized
and Monolithic
2
Hyper-specialized
Silo
3
Flexibility
and Agility
Data
engineers
Business
users
Data
stewards
ELT
Raw → Analytics
Business
moment

6. The gap between analytics data and business ready data
Data Producers Data Consumers
Business Ready Data
Business ready data is the final transformed version of the data that has
timely business logic and business context applied, that provides
the right insights to the right user at the right time.
Raw → Analytics ready → Business ready

7. Achieving business ready data with semantic layer
1
Decompose data
around domains
Distribute the ownership
with governance
2
Serve data as a
product
Delight the consumer with
ease of data discovery and
use
3
Enable Autonomy
Abstract technical complexity
4
Build an Ecosystem
Federated and global
governance

8. 8
The “dinosaur” in the room

9. Domain data owners achieve:
- centralized place for
decentralizing data for their
domains.
- Federated governance and
ecosystem.
Data consumers benefit:
- Trusted, single version of truth.
- Ease of data discovery and use
with their analytics tool of choice.
- Abstraction from technical
complexity with business ready
context.
Why Semantic Layer for Data Mesh?
Distributed ownership with governance

10. Key capabilities
1
Practical and agile
approach to semantic
modeling
2
The power of
providing centralized
governance
3
The opportunity to
create de-centralized
data products
▪ Dimensional analysis
▪ Different modeling
personas
▪ Composability with
conformed dimensions
▪ High performance
▪ Governed use of compute
▪ Consistency of metrics,
dimensions, models
▪ Excel for financial analysts
and ad-hoc analysis
▪ PBI/Tableau/Looker for
interactive dashboards
▪ Python for data science

11. Semantic Platform Deployment
11
SEMANTIC LAYER

12. Help
your
Data
Speak
the
Language
of
Your
Business
–
An
Overview
of
AtScale
©
2023
AtScale.
All
rights
reserved.
The AtScale Semantic Layer Platform
ANALYTICS
INTEGRATION
Native connection
to analytics layer
tools
SEMANTIC MODELING
Blend data sets, Engineer metrics, Define
business-oriented views of data
12
DATA
INTEGRATION
Connect to data
platforms, abstract
location & format of
data
QUERY VIRTUALIZATION
PERFORMANCE OPTIMIZATION
ANALYTICS GOVERNANCE
Translate analytics queries to optimized
SQL for execution on data platform
Dynamically orchestrate aggregates to
optimize performance and cost
Enforce access control and
data policies
BI TOOLS
EXCEL
NOTEBOOKS
AI / AUTOML
DATA APPS
FEATURE
STORES
DATA
WAREHOUSE
DATA LAKE
DATA VIRT
DATA
MARKETPLAC
E
Semantic Platform

13. Help
your
Data
Speak
the
Language
of
Your
Business
–
An
Overview
of
AtScale
©
2023
AtScale.
All
rights
reserved.
The AtScale Semantic Layer Platform
Combining Passive and Active Metadata
ANALYTICS
INTEGRATION
Native connection
to analytics layer
tools
SEMANTIC MODELING
Blend data sets, Engineer metrics, Define
business-oriented views of data
13
DATA
INTEGRATION
Connect to data
platforms, abstract
location & format of
data
QUERY VIRTUALIZATION
PERFORMANCE OPTIMIZATION
ANALYTICS GOVERNANCE
Translate analytics queries to optimized
SQL for execution on data platform
Dynamically orchestrate aggregates to
optimize performance and cost
Enforce access control and
data policies
BI TOOLS
EXCEL
NOTEBOOKS
AI / AUTOML
DATA APPS
FEATURE
STORES
DATA
WAREHOUSE
DATA LAKE
DATA VIRT
DATA
MARKETPLAC
E
Semantic Engine
Making Decisions Based on Active Metadata
▪ What is the data’s usage?
▪ What is the data’s popularity?
▪ What is the data affinity?
▪ What metrics and drill down paths are
used?
▪ Which queries have run?
▪ What has query performance been?
Semantic Layer
Providing Business Context with
Passive Metadata
▪ What does the data stand for?
▪ Who has access to the data?
▪ Where does the data come from?
▪ Is the data trustworthy?

14. PRESENTATION LAYER
SERVING
PROCESSING
INGESTION
MODELING
STORAGE
Modern Cloud Analytics Consumption
➔ Governed self service
(Descriptive + Predictive)
➔ Semantic engine monitors all
analytics queries and optimizes cost
and performance
➔ Users get access to all their data,
rather than running analytics on
subsets of data.
Semantic Platform

15. How
to
Use
a
Semantic
Layer
on
Big
Data
to
Drive
Al
&
BI
Impact
©
2022
AtScale.
All
rights
reserved.
How
to
Use
a
Semantic
Layer
on
Big
Data
to
Drive
Al
&
BI
Impact
©
2022
AtScale.
All
rights
reserved.
Time to Rethink Governance in the Age of Cloud Analytics
15
Governance
Going Beyond Data
2
1
3
Data Governance
Accountability framework to ensure the appropriate
behavior in the valuation, creation, consumption and
control of data and analytics.
Performance Governance
Optimize and govern the analytics
consumption performance based on actual
usage, instead of predefined usage patterns
Financial Governance
Monitor and govern cloud data usage with active
metadata.

16. Path to achieving data mesh and key capabilities
❏Define data domains and alignment with business domains
❏Combine data domains with business context to create data products
❏Register data products and made available for re-use based on business needs
❏Create the data mesh tissue by connecting the data domains via conform dimensions
❏Central governance with a federated approach given the responsibility to business domains

17. Help
your
Data
Speak
the
Language
of
Your
Business
–
An
Overview
of
AtScale
©
2023
AtScale.
All
rights
reserved.
17
Leading semantic layer platform for enterprise
analytics delivering:
▪ metrics layer for publishing full spectrum of
augmented analytics tied to live cloud data assets
▪ analytics workload optimization that enables tie to
live cloud data (no extracts, no caching)
▪ democratized and decentralized data product
innovation within a composable analytics framework
▪ seamless integration with leading analytics tools,
data platforms, and data fabric solutions
AtScale

18. Actionable
Insights
for
Everyone:
An
Overview
of
AtScale
©
2022
AtScale.
All
rights
reserved.
Sampling of AtScale Customers
18
Finserv Finserv Finserv Finserv Finserv Finserv Finserv Finserv Finserv Finserv
Finserv Finserv Insurance Insurance Insurance Insurance Insurance Insurance Insurance Insurance
Retail Retail Retail Retail Retail Retail Retail CPG / Mfg CPG / Mfg CPG / Mfg
CPG / Mfg CPG / Mfg CPG / Mfg CPG / Mfg Technology Technology Technology Technology Technology Technology
Bio / Pharma Bio / Pharma Bio / Pharma Bio / Pharma Other Other Other Other Other Other

19. Use Case
DATA CONSUMPTION
DATA WAREHOUSE
19
Problem: AFCU realized they couldn’t remain reliant on an outsourced analytics team and legacy analytics
infrastructure tools like ModelMax or Dundas BI to unearth insights from their data.
Solution: With AtScale’s semantic layer, AFCU was able to harness the power of dimensional modeling
with AtScale, standardizing dimensions, hierarchies, and attributes to present a unified set of data
regardless of the analytics toolset being used to access. By shielding users from the complexity of data
wrangling and engineering, this organization has given their internal teams a leg up and made self-service
BI a reality.
Key Benefits: Enable self-service BI, Create new data platform, Increase business agility
SEMANTIC LAYER

20. Use Case
DATA CONSUMPTION
SEMANTIC LAYER
DATA WAREHOUSE
20
Problem: Wayfair needed to drastically simplify their sprawling analytics infrastructure and had to maintain
business continuity through their transition to the cloud necessitated operating a hybrid on-
premises/cloud environment for a time.
Solution: With AtScale’s semantic layer, they have been able to accelerate their time-to-insight with a live
connection to their data at OLAP query speeds. Wayfair provides one unified & governed view of business
data for their hundreds of data modelers and business analysts.
Key Benefits: Expedited insights, integrated toolset, lower cost, faster and more consistent analytics

21. How
to
Use
a
Semantic
Layer
on
Big
Data
to
Drive
Al
&
BI
Impact
©
2022
AtScale.
All
rights
reserved.
How
to
Use
a
Semantic
Layer
on
Big
Data
to
Drive
Al
&
BI
Impact
©
2022
AtScale.
All
rights
reserved.
AtScale enables smarter decision-making by accelerating the flow of data-driven insights. The company’s semantic layer platform simplifies, accelerates, and extends business intelligence and data science capabilities
for enterprise customers across all industries. With AtScale, customers are empowered to democratize data, implement self-service BI and build a more agile analytics infrastructure for better, more impactful decision
making. For more information, please visit www.atscale.com and follow us on LinkedIn, Twitter or Facebook.
See AtScale in Action
https://www.atscale.com/demo/

22. Live Audience Q&A